Towards a privacy protection-capable noise fingerprinting for numerically aggregated data

Hu, Yun; Hu, Aiqun; Li, Chunguo; Zhang, Chunyu

doi:10.1016/j.cose.2022.102755

Cited by 6 publications

(11 citation statements)

References 17 publications

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“…• We evaluate the proposed mechanism using two reallife databases. Experiment results show that our mechanism (i) provides higher fingerprint robustness than a state-of-the-art database fingerprinting mechanism [35], and (ii) achieves higher database utility than the two-step methods (i.e., either local DP-based perturbation, data synthesis under central DP, or k-anonymity followed [35]) and the one-step approach (i.e., Gaussian noise-based fingerprinting [20]) by considering specific applications.…”

Section: Introductionmentioning

confidence: 97%

“…As a result, they end up changing a large amount of entries in the database and they significantly compromise the utility of the shared database (corroborated in Section VII). The only work that attempts to integrate privacy protection and fingerprinting is proposed in [20]. However, [20] injects continuous-valued Gaussian noise to the data, considers various combinations of variances as fingerprints, and relies on learning algorithms to fit the Gaussian noises.…”

Section: Introductionmentioning

confidence: 99%

“…The only work that attempts to integrate privacy protection and fingerprinting is proposed in [20]. However, [20] injects continuous-valued Gaussian noise to the data, considers various combinations of variances as fingerprints, and relies on learning algorithms to fit the Gaussian noises. Thus, [20] is vulnerable if a malicious SP compromises a large portion of fingerprinted data entries (shown in Section VII).…”

Section: Introductionmentioning

confidence: 99%

“…However, [20] injects continuous-valued Gaussian noise to the data, considers various combinations of variances as fingerprints, and relies on learning algorithms to fit the Gaussian noises. Thus, [20] is vulnerable if a malicious SP compromises a large portion of fingerprinted data entries (shown in Section VII). Besides, these works do not address the critical problem of controlling cumulative privacy loss if the same database is repeatedly shared with multiple SPs.…”

Section: Introductionmentioning

confidence: 99%

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Privacy-Preserving Database Fingerprinting

Ji¹,

Ayday²,

Yılmaz³

et al. 2023

Proceedings 2023 Network and Distributed System Security Symposium

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When sharing relational databases with other parties, in addition to providing high quality (utility) database to the recipients, a database owner also aims to have (i) privacy guarantees for the data entries and (ii) liability guarantees (via fingerprinting) in case of unauthorized redistribution. However, (i) and (ii) are orthogonal objectives, because when sharing a database with multiple recipients, privacy via data sanitization requires adding noise once (and sharing the same noisy version with all recipients), whereas liability via unique fingerprint insertion requires adding different noises to each shared copy to distinguish all recipients. Although achieving (i) and (ii) together is possible in a naïve way (e.g., either differentially-private database perturbation or synthesis followed by fingerprinting), this approach results in significant degradation in the utility of shared databases. In this paper, we achieve privacy and liability guarantees simultaneously by proposing a novel entrylevel differentially-private (DP) fingerprinting mechanism for relational databases without causing large utility degradation.The proposed mechanism fulfills the privacy and liability requirements by leveraging the randomization nature of fingerprinting and transforming it into provable privacy guarantees. Specifically, we devise a bit-level random response scheme to achieve differential privacy guarantee for arbitrary data entries when sharing the entire database, and then, based on this, we develop an ϵ-entry-level DP fingerprinting mechanism. We theoretically analyze the connections between privacy, fingerprint robustness, and database utility by deriving closed form expressions. We also propose a sparse vector technique-based solution to control the cumulative privacy loss when fingerprinted copies of a database are shared with multiple recipients.We experimentally show that our mechanism achieves strong fingerprint robustness (e.g., the fingerprint cannot be compromised even if the malicious database recipient modifies/distorts more than half of the entries in its received fingerprinted copy), and higher database utility compared to various baseline methods (e.g., application-dependent database utility of the shared database achieved by the proposed mechanism is higher than that of the considered baselines).

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Section: Introductionmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Privacy-Preserving Database Fingerprinting

Ji¹,

Ayday²,

Yılmaz³

et al. 2023

Proceedings 2023 Network and Distributed System Security Symposium

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“…The digital revolution and peer-to-peer networks have significantly influenced our daily lives, particularly digital information piracy [9]. Consumers may now access digital content and services at any time and from any location.…”

Section: Introductionmentioning

confidence: 99%

DWT Collusion Resistant Video Watermarking Using Tardos Family Codes

Rehman

Guelvouit

Dion

et al. 2022

2022 IEEE 5th International Conference on Image Processing Applications and Systems (IPAS)

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